web3_research_compass

web3_research_compass is a very small CLI helper that turns a project or idea name into a deterministic recommendation across three Web3 research directions:

• zk privacy rollups in the spirit of Aztec
• FHE compute stacks in the spirit of Zama
• soundness-first protocols with strong formal guarantees

It does not connect to any blockchain or RPC. It simply hashes the project name and uses that as a stable seed to assign weights to each track.

Repository layout

This repository intentionally contains only two files:

• app.py
• README.md

Concept

Teams often ask where to lean first:

• deeper into zk privacy and encrypted state
• deeper into fully homomorphic encryption and encrypted compute
• deeper into formal verification, proofs, and protocol soundness

This tool provides a playful but deterministic nudge. For a given project name, it:

1. Computes a SHA-256 hash of the name.
2. Uses the first few bytes of the hash to derive three weights.
3. Normalizes these weights to sum to 1.
4. Labels the highest weight as your suggested primary direction.

You can run it multiple times with different names or variants of a name to see how the focus shifts.

Installation

Requirements:

• Python 3.8 or newer

Setup steps:

1. Create a new GitHub repository with any name.
2. Place app.py and this README.md in the root directory.
3. Ensure python is available on your system path.
4. No external packages are required; the script uses only the standard library.

Usage

From the repository root, run the script with a project name.

Example usages:

• For a privacy-heavy idea, maybe called dark-market-l2
  Command: python app.py dark-market-l2

• For an encrypted analytics platform, maybe called fhe-metrics-hub
  Command: python app.py fhe-metrics-hub

• For a correctness-focused protocol, maybe called verified-rollup-lab
  Command: python app.py verified-rollup-lab

To integrate with other tools, you can request JSON output:

• Command: python app.py aztec-inspired-lab --json

Output

In human-readable mode, the script prints:

• the project name used as input
• three weights between 0.0 and 1.0 for:
  • zk privacy (Aztec-style)
  • FHE compute (Zama-style)
  • soundness-first protocols
• a suggested primary research direction, including:
  • short label
  • track key
  • one line description

In JSON mode, it returns a small object with:

• projectName
• primaryTrack
• primaryTrackName
• primaryDescription
• weights (a mapping with keys aztec, zama, soundness)

Notes

• The weights are deterministic: the same project name will always yield the same result.
• This is not a serious optimizer or planning tool. It is meant as a fun way to frame conversations about whether a project leans more toward Aztec-like privacy, Zama-like FHE compute, or soundness-first protocol work.
• You can edit the TRACKS dictionary in app.py to adjust descriptions or add your own internal labels.